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Field
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systems and models to enhance learning through AI technology. The PhD fellow will contribute to the Technological Advancement cluster by advancing synthetic data generation as a key work of AI LEARN’s
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to evolve advanced, human-centered AI technology to empower human learning, including designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will
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You hold a university Master’s degree in Neuroscience, Biomedical Sciences, Immunology or a related life science, and you are strongly motivated to develop human, animal-free models for pain. You
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this collaborative project, we will develop a comprehensive strategy combining (1) vasculature-on-chip models, (2) in-depth nanobubble characterization, and (3) tailored imaging solutions to advance cancer diagnostics
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-driven workload estimation, intelligent release planning, and explainable decision support. The PhD will operate across two worlds: The University of Twente — advancing scientific models, algorithms, and
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. The duration of the position is three years. Your work tasks The PhD project is centred around advanced CFD modelling of liquid green fuel (methanol and ammonia) combustion under engine-like conditions, i.e., in
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Structured scientific approach and enjoyment of independently developing models and research methods Our Offer: We work on the very latest issues that impact our society and are offering you the chance to
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phenomenon that remains quantitatively unresolved despite extensive modelling efforts. This Ph.D. project aims to advance a renormalisation-group (RG) field-theoretical framework that captures the universal
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The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en/offerta-formativa/dottorato/2025/computer-science About the
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serious safety risks for older adults, and rapid, reliable detection can significantly reduce long-term injury and improve emergency response. Building on recent advances in vibration-based sensing